Journal of Machine Learning Research最新文献_第10页

A Consistent Information Criterion for Support Vector Machines in Diverging Model Spaces. 发散模型空间中支持向量机的一致信息准则。

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2016-01-01

Xiang Zhang, Yichao Wu, Lan Wang, Runze Li

引用次数: 0

Fused Lasso Approach in Regression Coefficients Clustering - Learning Parameter Heterogeneity in Data Integration. 回归系数聚类的融合Lasso方法——数据集成中参数异质性的学习。

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2016-01-01

Lu Tang, Peter X K Song

{"title":"Fused Lasso Approach in Regression Coefficients Clustering - Learning Parameter Heterogeneity in Data Integration.","authors":"Lu Tang, Peter X K Song","doi":"","DOIUrl":"","url":null,"abstract":"As data sets of related studies become more easily accessible, combining data sets of similar studies is often undertaken in practice to achieve a larger sample size and higher power. A major challenge arising from data integration pertains to data heterogeneity in terms of study population, study design, or study coordination. Ignoring such heterogeneity in data analysis may result in biased estimation and misleading inference. Traditional techniques of remedy to data heterogeneity include the use of interactions and random effects, which are inferior to achieving desirable statistical power or providing a meaningful interpretation, especially when a large number of smaller data sets are combined. In this paper, we propose a regularized fusion method that allows us to identify and merge inter-study homogeneous parameter clusters in regression analysis, without the use of hypothesis testing approach. Using the fused lasso, we establish a computationally efficient procedure to deal with large-scale integrated data. Incorporating the estimated parameter ordering in the fused lasso facilitates computing speed with no loss of statistical power. We conduct extensive simulation studies and provide an application example to demonstrate the performance of the new method with a comparison to the conventional methods.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":"17 ","pages":""},"PeriodicalIF":6.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5647925/pdf/nihms872528.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35531942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Objective Markov Decision Processes for Data-Driven Decision Support. 用于数据驱动决策支持的多目标马尔可夫决策过程。

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2016-01-01 Epub Date: 2016-12-01

Daniel J Lizotte, Eric B Laber

引用次数: 0

Dimension-free concentration bounds on hankel matrices for spectral learning 用于谱学习的汉克尔矩阵的无维集中界

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2016-01-01 DOI: 10.5555/2946645.2946676

DenisFrançois, GybelsMattias, HabrardAmaury

引用次数: 1

Extracting PICO Sentences from Clinical Trial Reports using Supervised Distant Supervision. 利用远距离监督从临床试验报告中提取 PICO 句子

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2016-01-01

Byron C Wallace, Joël Kuiper, Aakash Sharma, Mingxi Brian Zhu, Iain J Marshall

{"title":"Extracting PICO Sentences from Clinical Trial Reports using Supervised Distant Supervision.","authors":"Byron C Wallace, Joël Kuiper, Aakash Sharma, Mingxi Brian Zhu, Iain J Marshall","doi":"","DOIUrl":"","url":null,"abstract":"Systematic reviews underpin Evidence Based Medicine (EBM) by addressing precise clinical questions via comprehensive synthesis of all relevant published evidence. Authors of systematic reviews typically define a Population/Problem, Intervention, Comparator, and Outcome (a PICO criteria) of interest, and then retrieve, appraise and synthesize results from all reports of clinical trials that meet these criteria. Identifying PICO elements in the full-texts of trial reports is thus a critical yet time-consuming step in the systematic review process. We seek to expedite evidence synthesis by developing machine learning models to automatically extract sentences from articles relevant to PICO elements. Collecting a large corpus of training data for this task would be prohibitively expensive. Therefore, we derive distant supervision (DS) with which to train models using previously conducted reviews. DS entails heuristically deriving 'soft' labels from an available structured resource. However, we have access only to unstructured, free-text summaries of PICO elements for corresponding articles; we must derive from these the desired sentence-level annotations. To this end, we propose a novel method - supervised distant supervision (SDS) - that uses a small amount of direct supervision to better exploit a large corpus of distantly labeled instances by learning to pseudo-annotate articles using the available DS. We show that this approach tends to outperform existing methods with respect to automated PICO extraction.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":"17 ","pages":""},"PeriodicalIF":6.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5065023/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140289407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graphical Models via Univariate Exponential Family Distributions. 通过单变量指数族分布建立图形模型

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2015-12-01

Eunho Yang, Pradeep Ravikumar, Genevera I Allen, Zhandong Liu

{"title":"Graphical Models via Univariate Exponential Family Distributions.","authors":"Eunho Yang, Pradeep Ravikumar, Genevera I Allen, Zhandong Liu","doi":"","DOIUrl":"","url":null,"abstract":"Undirected graphical models, or Markov networks, are a popular class of statistical models, used in a wide variety of applications. Popular instances of this class include Gaussian graphical models and Ising models. In many settings, however, it might not be clear which subclass of graphical models to use, particularly for non-Gaussian and non-categorical data. In this paper, we consider a general sub-class of graphical models where the node-wise conditional distributions arise from exponential families. This allows us to derive multivariate graphical model distributions from univariate exponential family distributions, such as the Poisson, negative binomial, and exponential distributions. Our key contributions include a class of M-estimators to fit these graphical model distributions; and rigorous statistical analysis showing that these M-estimators recover the true graphical model structure exactly, with high probability. We provide examples of genomic and proteomic networks learned via instances of our class of graphical models derived from Poisson and exponential distributions.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":"16 ","pages":"3813-3847"},"PeriodicalIF":6.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4998206/pdf/nihms808903.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34398019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery. 应用于神经语义基础发现的校准多元回归。

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2015-08-01

Han Liu, Lie Wang, Tuo Zhao

{"title":"Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery.","authors":"Han Liu, Lie Wang, Tuo Zhao","doi":"","DOIUrl":"","url":null,"abstract":"We propose a calibrated multivariate regression method named CMR for fitting high dimensional multivariate regression models. Compared with existing methods, CMR calibrates regularization for each regression task with respect to its noise level so that it simultaneously attains improved finite-sample performance and tuning insensitiveness. Theoretically, we provide sufficient conditions under which CMR achieves the optimal rate of convergence in parameter estimation. Computationally, we propose an efficient smoothed proximal gradient algorithm with a worst-case numerical rate of convergence O(1/ϵ), where ϵ is a pre-specified accuracy of the objective function value. We conduct thorough numerical simulations to illustrate that CMR consistently outperforms other high dimensional multivariate regression methods. We also apply CMR to solve a brain activity prediction problem and find that it is as competitive as a handcrafted model created by human experts. The R package camel implementing the proposed method is available on the Comprehensive R Archive Network http://cran.r-project.org/web/packages/camel/.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":"16 ","pages":"1579-1606"},"PeriodicalIF":6.0,"publicationDate":"2015-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5354374/pdf/nihms-752268.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34832057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Should we really use post-hoc tests based on mean-ranks? 我们真的应该使用基于平均秩的事后检验吗?

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2015-05-09 DOI: 10.5555/2946645.2946650

BenavoliAlessio, CoraniGiorgio, MangiliFrancesca

{"title":"Should we really use post-hoc tests based on mean-ranks?","authors":"BenavoliAlessio, CoraniGiorgio, MangiliFrancesca","doi":"10.5555/2946645.2946650","DOIUrl":"https://doi.org/10.5555/2946645.2946650","url":null,"abstract":"The statistical comparison of multiple algorithms over multiple data sets is fundamental in machine learning. This is typically carried out by the Friedman test. When the Friedman test rejects the null hypothesis, multiple comparisons are carried out to establish which are the significant differences among algorithms. The multiple comparisons are usually performed using the mean-ranks test. The aim of this technical note is to discuss the inconsistencies of the mean-ranks post-hoc test with the goal of discouraging its use in machine learning as well as in medicine, psychology, etc. We show that the outcome of the mean-ranks test depends on the pool of algorithms originally included in the experiment. In other words, the outcome of the comparison between algorithms A and B depends also on the performance of the other algorithms included in the original experiment. This can lead to paradoxical situations. For instance the difference between A and B could be declared significant if the pool comprises algorithms C, D, E and not significant if the pool comprises algorithms F, G, H. To overcome these issues, we suggest instead to perform the multiple comparison using a test whose outcome only depends on the two algorithms being compared, such as the sign-test or the Wilcoxon signed-rank test.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":"1 1","pages":"5:1-5:10"},"PeriodicalIF":6.0,"publicationDate":"2015-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71138343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 302

The flare Package for High Dimensional Linear Regression and Precision Matrix Estimation in R. 用于 R 中高维线性回归和精度矩阵估计的 flare 软件包。

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2015-03-01

Xingguo Li, Tuo Zhao, Xiaoming Yuan, Han Liu

引用次数: 0

Fast cross-validation via sequential testing 通过顺序测试快速交叉验证

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2015-01-01 DOI: 10.5555/2789272.2886786

KruegerTammo, PankninDanny, BraunMikio

引用次数: 2